基于灰色马尔科夫模型的采煤机自适应截割策略研究

Research of Adaptive Cutting Strategy of Shearer Based on Gray Markov Model

借助灰色马尔科夫组合模型对传统的采煤机记忆截割策略进行优化和改进,以提高采煤机截割作业的效率和精度。通过灰色模型对采煤机截割高度预测数据进行残差计算和状态分析并利用马尔科夫链状态概率矩阵修正灰色模型,最后通过仿真分析可知,基于灰色马尔科夫模型的采煤机自适应截割策略控制精度以及截割作业自动化水平均较高。

By means of the combination of gray markov model to optimize and improve the traditional coal winning machine memory cutting strategy for improving the precision and efficiency of coal winning machine cutting operation. By gray model we can do residual calculation and state analysis to the predict cutting height data of the coal winning machine and modify the gray model by using markov chain state probability matrix. At last, by simulation and analysis, we can know that the shearer adaptive cutting strategy based on gray markov model has highter control precision and automation level in cutting operations.